外骨骼
均方误差
接头(建筑物)
计算机科学
人工智能
膝关节
康复
传感器融合
平均绝对误差
肌电图
运动(物理)
物理医学与康复
模式识别(心理学)
数学
模拟
统计
物理疗法
医学
工程类
结构工程
外科
作者
Junyu Han,Haoping Wang,Yang Tian
标识
DOI:10.23919/ccc58697.2023.10240374
摘要
Active rehabilitation training for patients with lower limb motor dysfunction mainly depends on exoskeleton robots that can accurately and timely identify human motion intentions, which can be implemented by continues estimation of human joint angles. In this paper, a multi model fusion based ridge regression named as ‘MMF-RR’ for the prediction of human joint angles is proposed. Specifically, four selected basic learners are first end-to-end trained for the subsequent analysis from the data set composed of surface electromyography signals (sEMG) and historical angles. Then the results are spliced into ridge regression with penalty terms for joint angle prediction. The proposed MMF-RR was evaluated on publicly available dataset for 22 participants, where half of them were healthy participants, while the other half had various knee joint diseases. And the results show that the proposed MMF-RR can significantly improve the predicting performance. In particular, the average mean absolute error (MAE) and average mean square error (MSE) of knee joint angle prediction of healthy participants and participants with knee joint lesions were 6.73%, 2.36% and 8.97%, 3.26%, respectively, which indicate that MMF-RR can provide more precise human motion intention information for lower limb motor under rehabilitation training than five comparison methods.
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